Lopez C. Machine Learning. Supervised Learning Techniques...2022.pdf

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MACHINE LEARNING.
SUPERVISED LEARNING TECHNIQUES: REGRESSION.
Examples with SAS and MATLAB
César Pérez López
CONTENTS
INTRODUCTION TO MACHINE LEARNING
1.1 INTRODUCTION TO MACHINE LEARNING
1.2 SUPERVISED LEARNING
1.3 UNSUPERVISED LEARNING
1.4 SELECT ALGORITHMS
WORKING WITH SAS ENTERPRISE MINER
2.1 SAS Enterprise MINER ENVIRONMENT
2.2 Starting with SAS Enterprise Miner
2.2.1 Create a New Project
2.2.2 Project Start Code
2.2.3 Create a New Process Flow Diagram
2.2.4 Create a Data Source
2.2.5 Connect Nodes in Diagram Workspace
2.2.6 Run the Process Flow Diagram
2.2.7 View Results
2.2.8 Create A Model Package
2.3 SAS Enterprise Miner User Interface
2.3.1 Sas Enterprise Miner main menu
2.3.2 The SAS Enterprise Miner Node Toolbar
SUPERVISED LEARNING
REGRESSION
techniques
with
sas
enterprise
miner:
3.1 SUPERVISED LEARNING TECHNIQUES with SAS ENTERPRISE
MINER
3.2 Regression node: multiple regression model
3.2.1 Regression Node Data Set Requirements
3.2.2 Regression Node Train Properties: Equation
3.2.3 Regression Node Train Properties: Class Targets
3.2.4 Regression Node Train Properties: Model Options
3.2.5 Regression Node Train Properties: Model Selection
3.2.6 Regression Node Train Properties: Optimization Options
3.2.7 Regression Node Train Properties: Convergence Criteria
3.2.8 Regression Node Train Properties: Output Options
3.2.9 Example 1. Regression
3.2.10 Example 2. Logistic Regression
3.3 STEPWISE REGRESSION: Dmine Regression Node
3.3.1 Dmine Regression Node Data Set Requirements
3.3.2 Dmine Regression Node Results
3.4 MODELS WITH MULTICOLINEARITY: Partial Least Squares Node
3.4.1 Partial Least Squares Node Algorithm
3.4.2 Partial Least Squares Node and Missing Data Set Values
3.4.3 Partial Least Squares Node Data Set Results
3.4.4 Partial Least Squares Node Cross-Validation
3.4.5 Partial Least Squares Node Train Properties: Modeling Techniques
3.4.6 Partial Least Squares Node Train Properties: Number of Factors
3.4.7 Partial Least Squares Node Train Properties: Cross Validation
3.4.8 Partial Least Squares Node Train Properties: Random CV Options
3.4.9 Partial Least Squares Node Train Properties: Variable Selection
3.4.10 Partial Least Squares Node Status Properties
3.4.11 Partial Least Squares Node Results
3.4.12 Partial Least Squares Node Example
3.5 LARS Node
3.5.1 LARs Node Train Properties: Modeling Techniques
3.5.2 LARs Node Train Properties: Cross Validation Options
3.5.3 LARs Node Train Properties: Report
3.5.4 LARs Node Score Properties
3.5.5 LARs Node Status Properties
3.5.6 LARs Node Results
3.5.7 LARs Node Example
SUPERVISED
LEARNING
TECHNIQUES
WITH
NETWORKS IN SAS ENTERPRISE MINER: REGRESION
NEURAL

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